Constraints on neutron star superfluidity from the cooling neutron star in Cassiopeia A using all <i>Chandra</i> ACIS-S observations
Bibliographic record
Abstract
ABSTRACT Analysis of Chandra observations of the neutron star (NS) in the centre of the Cassiopeia A supernova remnant taken in the subarray (FAINT) mode of the Advanced CCD Imaging Spectrometer (ACIS) detector performed by Posselt and collaborators revealed, after inclusion of the most recent (2020 May) observations, a significant decrease of the source surface temperature from 2006 to 2020. The obtained cooling rate is consistent with those obtained from analysis of the 2000–2019 data taken in the GRADED mode of the ACIS detector, which is potentially more strongly affected by instrumental effects. We performed a joint spectral analysis using all ACIS data to constrain the NS parameters and cooling rate. We constrain the mass of the Cassiopeia A NS at $M=1.55\pm 0.25\, {\rm M}_\odot$, and its radius at R = 13.5 ± 1.5 km. The surface temperature cooling rate is found to be 2.2 ± 0.3 per cent in 10 yr if the absorbing hydrogen column density is allowed to vary and 1.6 ± 0.2 per cent in 10 yr if it is fixed. The observed cooling can be explained by enhanced neutrino emission from the superfluid NS interior due to Cooper pair formation (CPF) process. Based on analysis of all ACIS data, we constrain the maximal critical temperature of triplet neutron pairing within the NS core at (4–9.5) × 108 K. In accordance with previous studies, the required effective strength of the CPF neutrino emission is at least a factor of 2 higher than existing microscopic calculations suggest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".